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      Associations between insomnia and pregnancy and perinatal outcomes: Evidence from mendelian randomization and multivariable regression analyses

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          Abstract

          Background

          Insomnia is common and associated with adverse pregnancy and perinatal outcomes in observational studies. However, those associations could be vulnerable to residual confounding or reverse causality. Our aim was to estimate the association of insomnia with stillbirth, miscarriage, gestational diabetes (GD), hypertensive disorders of pregnancy (HDP), perinatal depression, preterm birth (PTB), and low/high offspring birthweight (LBW/HBW).

          Methods and findings

          We used 2-sample mendelian randomization (MR) with 81 single-nucleotide polymorphisms (SNPs) instrumenting for a lifelong predisposition to insomnia. Our outcomes included ever experiencing stillbirth, ever experiencing miscarriage, GD, HDP, perinatal depression, PTB (gestational age <37 completed weeks), LBW (<2,500 grams), and HBW (>4,500 grams). We used data from women of European descent ( N = 356,069, mean ages at delivery 25.5 to 30.0 years) from UK Biobank (UKB), FinnGen, Avon Longitudinal Study of Parents and Children (ALSPAC), Born in Bradford (BiB), and the Norwegian Mother, Father and Child Cohort (MoBa). Main MR analyses used inverse variance weighting (IVW), with weighted median and MR-Egger as sensitivity analyses. We compared MR estimates with multivariable regression of insomnia in pregnancy on outcomes in ALSPAC ( N = 11,745). IVW showed evidence of an association of genetic susceptibility to insomnia with miscarriage (odds ratio (OR): 1.60, 95% confidence interval (CI): 1.18, 2.17, p = 0.002), perinatal depression (OR 3.56, 95% CI: 1.49, 8.54, p = 0.004), and LBW (OR 3.17, 95% CI: 1.69, 5.96, p < 0.001). IVW results did not support associations of insomnia with stillbirth, GD, HDP, PTB, and HBW, with wide CIs including the null. Associations of genetic susceptibility to insomnia with miscarriage, perinatal depression, and LBW were not observed in weighted median or MR-Egger analyses. Results from these sensitivity analyses were directionally consistent with IVW results for all outcomes, with the exception of GD, perinatal depression, and PTB in MR-Egger. Multivariable regression showed associations of insomnia at 18 weeks of gestation with perinatal depression (OR 2.96, 95% CI: 2.42, 3.63, p < 0.001), but not with LBW (OR 0.92, 95% CI: 0.69, 1.24, p = 0.60). Multivariable regression with miscarriage and stillbirth was not possible due to small numbers in index pregnancies. Key limitations are potential horizontal pleiotropy (particularly for perinatal depression) and low statistical power in MR, and residual confounding in multivariable regression.

          Conclusions

          In this study, we observed some evidence in support of a possible causal relationship between genetically predicted insomnia and miscarriage, perinatal depression, and LBW. Our study also found observational evidence in support of an association between insomnia in pregnancy and perinatal depression, with no clear multivariable evidence of an association with LBW. Our findings highlight the importance of healthy sleep in women of reproductive age, though replication in larger studies, including with genetic instruments specific to insomnia in pregnancy are important.

          Abstract

          Using Mendelian randomization and observational analyses, Qian Yang and colleagues investigate the associations between insomnia and stillbirth, miscarriage, gestational diabetes, hypertensive disorders of pregnancy, perinatal depression, preterm birth, and low and high birth weight.

          Author summary

          Why was this study done?
          • Insomnia in pregnancy was associated with higher risks of adverse pregnancy and perinatal outcomes in observational studies.

          • It is currently not clear whether insomnia causes adverse pregnancy and perinatal outcomes or whether the unfavourable associations are explained by confounding.

          • To the best of our knowledge, mendelian randomization (MR) has not been used to explore whether there is evidence to support a causal association between insomnia and adverse pregnancy and perinatal outcomes.

          What did the researchers do and find?
          • We used data on up to 356,069 women from UK Biobank (UKB), FinnGen, and 3 birth cohorts and assessed whether genetic susceptibility to insomnia was associated with stillbirth, miscarriage, gestational diabetes (GD), hypertensive disorders of pregnancy (HDP), perinatal depression, preterm birth (PTB), low offspring birthweight (LBW), and high offspring birthweight (HBW) in 2-sample MR.

          • To triangulate with our MR estimates, we conducted multivariable regression in 11,745 women from the Avon Longitudinal Study of Parents and Children (ALSPAC), where insomnia was measured in pregnancy for all outcomes except miscarriage and stillbirth for which there were too few cases in the index pregnancy.

          • We found evidence from MR and multivariable regression that insomnia was associated with a higher risk of perinatal depression, and MR analyses also suggested evidence for an association between genetically predicted insomnia and risks of miscarriage and LBW.

          What do these findings mean?
          • These findings raise the possibility that insomnia maybe related to adverse pregnancy outcomes, implying that interventions to improve healthy sleep may be beneficial to a healthy pregnancy.

          • Key limitations of our study are potential horizontal pleiotropy (particularly for perinatal depression) and low statistical power in MR and residual confounding in multivariable regression. Replication in larger MR studies would be valuable.

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          Most cited references65

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          Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

          Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
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            Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

            ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.
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              The MR-Base platform supports systematic causal inference across the human phenome

              Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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                Author and article information

                Contributors
                Role: Formal analysisRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Funding acquisitionRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: Project administrationRole: Writing – review & editing
                Role: Funding acquisitionRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                PLoS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                6 September 2022
                September 2022
                : 19
                : 9
                : e1004090
                Affiliations
                [1 ] MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
                [2 ] Population Health Sciences, Bristol Medical School, University of Bristol, United Kingdom
                [3 ] Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
                [4 ] Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom
                [5 ] National Institute for Health Research Bristol Biomedical Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, United Kingdom
                The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, UNITED KINGDOM
                Author notes

                I have read the journal’s policy and the authors of this manuscript have the following competing interests: KT has acted as a consultant for CHDI Foundation, and Expert Witness to the High Court in England, called by the UK Medicines and Healthcare products Regulatory Agency, defendants in a case on hormonal pregnancy tests and congenital anomalies 2021/22. DAL has received support from Medtronic LTD and Roche Diagnostics for biomarker research that is not related to the study presented in this paper. The other authors report no conflicts.

                Author information
                https://orcid.org/0000-0001-8778-4132
                https://orcid.org/0000-0001-5188-5775
                https://orcid.org/0000-0002-0568-3774
                https://orcid.org/0000-0002-3551-2829
                https://orcid.org/0000-0003-2763-4647
                https://orcid.org/0000-0002-5770-8363
                https://orcid.org/0000-0001-9572-7293
                https://orcid.org/0000-0002-6793-2262
                Article
                PMEDICINE-D-21-04244
                10.1371/journal.pmed.1004090
                9488815
                36067251
                15d92139-f81a-4024-87c3-49402b019f61
                © 2022 Yang et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 7 October 2021
                : 15 August 2022
                Page count
                Figures: 3, Tables: 2, Pages: 20
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MM_UU_00011/1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MM_UU_00011/3
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MM_UU_00011/6
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100002860, China Sponsorship Council;
                Award ID: CSC201808060273
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/P014054/1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100005416, Norges Forskningsråd;
                Award ID: 262700
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 947684
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000274, British Heart Foundation;
                Award ID: FS/17/37/32937
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000274, British Heart Foundation;
                Award ID: CH/F/20/90003
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Award ID: NF-0616-10102
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: 217065/Z/19/Z
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: WT088806
                Funded by: 23andMe
                Funded by: funder-id http://dx.doi.org/10.13039/100010269, Wellcome Trust;
                Award ID: WT223601/Z/21/Z
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: WT101597MA
                Funded by: funder-id http://dx.doi.org/10.13039/501100000269, Economic and Social Research Council;
                Award ID: MR/N024397/1
                Funded by: funder-id http://dx.doi.org/10.13039/501100000274, British Heart Foundation;
                Award ID: CS/16/4/32482
                Funded by: funder-id http://dx.doi.org/10.13039/501100014338, National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber;
                Award ID: NIHR200166
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: G0600705
                Funded by: funder-id http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Award ID: NF-SI-0611-10196
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01DK10324
                Funded by: funder-id http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 669545
                Funded by: funder-id http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: FP7/2007-2013
                QY, MCB, ES, FK, ALS, KT and DAL work in a unit that is supported by the University of Bristol and UK Medical Research Council (MRC, MM_UU_00011/1, MM_UU_00011/3 to KT and MM_UU_00011/6 to DAL). This work was supported by China Scholarship Council PhD Scholarship (CSC201807060273 to QY), UK MRC Skilled Development Fellowship (MR/P014054/1 to MCB), Research Council of Norway through its Centres of Excellence funding scheme (262700 to MCM and SHE), European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (947684 to MCM), and British Heart Foundation (BHF) Immediate Postdoctoral Basic Science Research Fellowship (FS/17/37/32937 to PJC). DAL is a BHF Chair (CH/F/20/9003) and National Institute of Health Research (NIHR) Senior Investigator (NF-0616-10102). Core funding for ALSPAC is provided by UK MRC and University of Bristol (217065/Z/19/Z). Genotyping of maternal samples was funded by the Wellcome Trust (WT088806), and offspring samples were genotyped by Sample Logistics and Genotyping Facilities at the Wellcome Trust Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe. A comprehensive list of grants funding is available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). BiB is supported by a Wellcome programme grant (WT223601/Z/21/Z: Age of Wonder), an infrastructure grant (WT101597MA), a UK MRC and Economic and Social Science Research Council programme grant (MR/N024397/1), a BHF Clinical Study grant (CS/16/4/32482), and NIHR under its Applied Health Research Collaboration Yorkshire and Humber (NIHR200166) and the NIHR Clinical Research Network. Further supports for genome-wide and multiple omics measurements in BiB are from UK MRC (G0600705), NIHR (NF-SI-0611010196), US National Institute of Health (R01DK10324), and ERC via Advanced Grant (669545) and under the European Union’s Seventh Framework Programme (FP7/2007-2013). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Medical Conditions
                Sleep Disorders
                Dyssomnias
                Insomnia
                Medicine and Health Sciences
                Neurology
                Sleep Disorders
                Dyssomnias
                Insomnia
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Pregnancy
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Pregnancy
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Pregnancy
                Pregnancy Complications
                Miscarriage
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Pregnancy
                Pregnancy Complications
                Miscarriage
                Biology and Life Sciences
                Genetics
                Single Nucleotide Polymorphisms
                Medicine and Health Sciences
                Vascular Medicine
                Blood Pressure
                Hypertension
                Hypertensive Disorders in Pregnancy
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Pregnancy
                Hypertensive Disorders in Pregnancy
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Pregnancy
                Hypertensive Disorders in Pregnancy
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Birth
                Preterm Birth
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Birth
                Preterm Birth
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Pregnancy
                Pregnancy Complications
                Preterm Birth
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Pregnancy
                Pregnancy Complications
                Preterm Birth
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Stillbirths
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Human Genetics
                Genome-Wide Association Studies
                Custom metadata
                vor-update-to-uncorrected-proof
                2022-09-20
                We used both individual participant cohort data and publicly available summary statistics. We present summary statistics that we generated from those individual participant cohort data in S4 and S5 Tables. Full information on how to access UKB data can be found at its website ( https://www.ukbiobank.ac.uk/researchers/). All ALSPAC data are available to scientists on request to the ALSPAC Executive via this website ( http://www.bristol.ac.uk/alspac/researchers/), which also provides full details and distributions of the ALSPAC study variables. Similarly, data from BiB are available on request to the BiB Executive ( https://borninbradford.nhs.uk/research/how-to-access-data/). Data from MoBa are available from the Norwegian Institute of Public Health after application to the MoBa Scientific Management Group (see its website https://www.fhi.no/en/op/data-access-from-health-registries-health-studies-and-biobanks/data-access/applying-for-access-to-data/ for details). Summary statistics from FinnGen are publicly available on its website ( https://finngen.gitbook.io/documentation/data-download).

                Medicine
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